Network Models and Optimization: Multiobjective Genetic Algorithm ApproachNetwork models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing. Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems. Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems. |
From inside the book
Results 1-5 of 83
... Formulation of the Multi-stage Logistics ....176 3.4.2 Priority-based GA for the Multi-stage Logistics ... Formulation of the Flexible Logistics Model . 196 3.5.2 Direct Path-based GA for the Flexible Logistics Model . . . . 202 3.5.3 ...
... ...............457 6.4.1 Mathematical Formulation of rc-PSP/mM Models . . .......457 6.4.2 Adaptive Hybrid GA for rc-PSP/mM Models . . ...........461 6.4.3 NumericalExperiment................................470 6.5 Summary ................
... Optimization: Formulation, Discussion and Generalization, Proceeding 5th International Conference on GAs, 416–423. 54. Srinivas, N. & Deb, K. (1995). Multiobjective Function Optimization 46 1 Multiobjective Genetic Algorithms.
... formulated as some sort of a combinatorial optimization problem. 2.2.1. Mathematical. Formulation. of. the. SPP. Models. Let G =(N,A) be a directed network, which consists of a finite set of nodes N = {1, 2, ···, n} and a set of directed ...
... formulated as follows in the form of integer programming: min z = n. ∑. i=1 n. ∑. j=1 c ij x ij (2.1) s.t. n. ∑. xij− n. ∑. xki= ⎧ ⎨ ⎩ 1 (i=1) 0 (i = 2,3,···,n−1) (2.2) j=1 k=1 −1 (i = n) xij = 0or1 ∀ i, j (2.3) 2.2.
Contents
1 | |
49 | |
Logistics Network Models | 135 |
Communication Network Models | 229 |
Advanced Planning and Scheduling Models | 297 |
Project Scheduling Models | 419 |
Assembly Line Balancing Models | 477 |
Tasks Scheduling Models | 551 |
References | 604 |
Index | 687 |
Other editions - View all
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2008 |
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2010 |